Approved Research

Integrated High throughput profilings based on Machine Learning to identify the efficacy and safety of cancer patients treated with therapies

Lay summary

Living organisms experience millions of signals transferred every second between cells, tissues, organs, and external environmental stimuli. Fine-tuned responses at various degrees and scales within the human body are central to the homeostatic mechanism that copes with potentially harmful environmental perturbations, including pathogens, smoking, and drugs, and interacts with the genetic background arising from spontaneous somatic mutations and numerous germline variants. Thus, a holistic view of homeostatic mechanisms through the study of genomic and epigenetic aberrations is needed to understand the core of cancer biology and the pathophysiological features of cancer during oncogenesis and tumor progression.

A multi-omics study is a data-driven scientific investigation that analyzes a range of high-dimensional datasets at multiple levels and scales to reveal the complexity of cells and their environment. Such type of study can provide novel frameworks to untangle biological phenomena or models to test certain hypotheses using various datasets. In cancer research, a paradigm shift toward multi-omics approaches has been achieved with the recent development of high-throughput technologies in genomics and transcriptomics, increasing effort in large-scale research collaboration, and advancement of computational algorithms. Together with advances in genomics and transcriptomics, proteomics is emerging as a prominent field to elucidate the dynamics of gene activity. Large-scale proteomic research, such as that promoted by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), has uncovered the ubiquitous link of biomolecules to the environment and disease status. Multi-omics approaches have been applied to numerous clinical studies for better identification of clinical subtypes or drug resistance, prediction of effective combination therapies, and identification of predictive biomarkers to increase the response rate to targeted treatments.

Hence, this study aims to use high-throughput production of omics data has led to an increase of data sets of different types that need to be integrated in order to better understand disease mechanisms and how these multiple molecular data generate the observed phenotypes in complex diseases. Merging imaging phenotypes with multi-omic biological data may lead to new prognostic cancer models, new support for patient treatment strategy, and the development of improved survival predictors. The identified predictors will be used to monitor treatment outcomes and decide on treatment termination to improve cancer treatment outcomes. The identified predictors will be subject to validation in future prospective studies. The goal is to apply these biomarkers in future clinical practice to improve cancer treatment outcomes.